Daniel Fried
Pittsburgh, Pennsylvania, United States
586 followers
468 connections
About
https://dpfried.github.io/
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Allison Sullivan
If you didn't get a chance to attend the NSF CISE Career Workshop the slides from all of ours talks are posted on the materials page: https://lnkd.in/g3dRcxEx And Josiah made a great Overleaf template: https://lnkd.in/gNdUG2kY Some key takeaways from our CAREER Awardee Talks: 1. The first page should tell your whole story - this is the first impression for the reviewer. You don't want them to have to look any further into the proposal to be sold on your idea. 2. We all had different degrees of preliminary work. Unfortunately, I don't think there is a hard metric for this - and I think it further emphasizes that the CAREER is more about your vision than anything else. 3. Have a baby? Sleep deprivation may lead to transformative research ideas - caveat: this is based on a small sample size (3 out of 4) so maybe not a strong recommendation d=
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Andres Agostini → GOOGLE C-Level Scholar → Englishtronium
BY CORNELL UNIVERSITY (QUANTUM PHYSICS): “Demonstration of logical qubits and repeated error correction with better-than-physical error rates! The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a trapped-ion QCCD processor where, through the use of fault-tolerant encoding and error correction, we are able to suppress logical error rates to levels below the physical error rates. In particular, we entangled logical qubits encoded in the [[7,1,3]] code with error rates 9.8 times to 500 times lower than at the physical level, and entangled logical qubits encoded in a [[12,2,4]] code with error rates 4.7 times to 800 times lower than at the physical level, depending on the judicious use of post-selection. Moreover, we demonstrate repeated error correction with the [[12,2,4]] code, with logical error rates below physical circuit baselines corresponding to repeated CNOTs, and show evidence that the error rate per error correction cycle, which consists of over 100 physical CNOTs, approaches the error rate of two physical CNOTs. These results signify an important transition from noisy intermediate scale quantum computing to reliable quantum computing, and demonstrate advanced capabilities toward large-scale fault-tolerant quantum computing. ........... SOURCE: https://lnkd.in/ecNNHT-B
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Sriparna Saha
I am excited to share that our paper, "Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?" has been accepted for publication in IEEE Transactions on Artificial Intelligence (IEEE TAI)! Abstract: In the age of advanced generative AI, distinguishing truth from deception is a growing societal challenge. Our research explores how Large Language Models (LLMs) can aid in detecting misinformation. We use a comprehensive approach, evaluating multiple LLMs with few and zero-shot prompts and incorporating sentiment and emotion annotations to understand their impact on misinformation detection. Additionally, we deliberately distort news using ChatGPT to examine various parameters like abstractness and concreteness, enhancing our framework for identifying authentic, human-generated, and LLM-induced misinformation. Impact Statement: As LLMs evolve, they play a dual role as both defenders and perpetrators of misinformation. This research delves into the conflicting nature of LLMs, evaluating their effectiveness across diverse datasets and prompting techniques. By corrupting authentic news and human-generated fake news with ChatGPT, we quantify parameters like concreteness and abstractness to improve misinformation detection tools. Our findings aim to enhance current frameworks and contribute to the responsible development of AI, fostering a more reliable and resilient information ecosystem in the face of misinformation. Key Contributions: LLM Evaluation: Assessed multiple LLMs' effectiveness in detecting rumor content across six datasets. Prompting Techniques: Investigated few-shot and zero-shot prompting and examined sensitivity to hyperparameters. Sentiment & Emotion: Explored the impact of sentiment and emotion on LLMs' rumor detection capabilities. Dual Corruption Approach: Intentionally distorting both authentic news and human-generated misinformation using ChatGPT. We analyzed parameters such as concreteness and named entity density to gain deeper insights into the impact of these distortions. Explore our research and access the code on GitHub: https://lnkd.in/gbY-9QcT A huge thank you to my co-authors Raghvendra Kumar, Bhargav Goddu, Adam Jatowt, and all those who supported this journey. Here's to advancing the fight against misinformation with cutting-edge AI technologies! #Research #IEEE #ArtificialIntelligence #MisinformationDetection #LargeLanguageModels #AI #FakeNews #IEEE_TAI
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Priyadarshini Panda
[PAPER UPDATE] Excited to announce that our paper "When In-Memory Computing Meets Spiking Neural Networks: From Applications to Circuits" has been published in #AppliedPhysicsReviews! 🔥Curious about the trade-offs that shape cutting-edge designs? How do neuro-inspired spiking neural networks (SNNs) algorithms and In-memory Computing (IMC) revolutionize the future of AI? This paper dives deep into their synergies, investigating the complex interactions between accuracy, energy, latency, and area. Major Contributions: - Comprehensive overview of SNNs + IMC applications & circuits powering neuromorphic computing. - Exploiting SNN temporal sparsity for analog IMC with massive PPA gains—featuring #SpikeSim, an SNN-specific IMC architecture. - Tackling NVM non-idealities in IMC crossbars with solutions like noise-aware batchnorm, ensuring robust SNN inference. - Challenging the myth: "More NVM precision = better," highlighting trade-offs in energy efficiency, and area. -Future-forward insights on online learning, emerging neuronal devices, and co-exploration to drive energy-efficient IMC-SNN in next-gen AI hardware. Authors: Abhishek Moitra Abhiroop Bhattacharjee Yuhang Li Youngeun Kim Paper Link: https://lnkd.in/eDtFavrg SpikeSim Code: https://lnkd.in/eBz5ZnRD #NeuromorphicComputing #InMemoryComputing #SpikingNeuralNetworks #APR #CoDesign Center for the Co-Design of Cognitive Systems
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Dr. Aditya Raj
A recent breakthrough titled "Matrix Multiplication-Free LLMs" demonstrates a huge advancement in the area of Large Language Models (LLMs) by reducing computational costs. The authors have eliminated MatMul operations from LLMs, claiming to 10 times reduction in memory usage and a 25.6% increase in training speed, all while maintaining strong performance at billion-parameter scales. Paper link: https://lnkd.in/ggph8qXc #AI #machinelearning #deeplearning #LLMs
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